Aims

This module focuses on digital image processing. It first introduces the digital image, with a description of how digital images are captured and represented. It then goes on to cover algorithms for image characterisation, manipulation, segmentation and feature extraction in direct space. The course then proceeds to cover image filtering techniques with some indication of the role and implications of Fourier space, and more advanced characterisation and feature detection techniques such as edge and corner detection, together with multi-resolution methods, treatment of colour images, template matching and optical flow techniques. The course has a strong practical component that allows students to explore a range of practical techniques by implementing their own image processing tools using Matlab or Python

Learning outcomes

On successful completion of the module, a student will be able to:

Understand (i.e., be able to describe, analyse and reason about) how digital images are represented (in the spatial and frequency domain), manipulated, encoded and processed, with emphasis on algorithm design, implementation and performance evaluation.

Availability and prerequisites

This module delivery is available for selection on the below-listed programmes. The relevant programme structure will specify whether the module is core, optional, or elective.

In order to be eligible to select this module as optional or elective, where available, students must meet all prerequisite conditions to the satisfaction of the module leader. Places for students taking the module as optional or elective are limited and will be allocated according to the department’s module selection policy.

Programmes on which available:

MRes Robotics

MRes Virtual Reality

MSc Computer Graphics, Vision and Imaging

MSc Robotics and Computation

MRes Medical Physics and Biomedical Engineering

Prerequisites:

There are no formal prerequisites.

Content

Introduction to the digital image

Why digital images?

Digital image capture

Data types and 2D representation of digital images.

Characteristics of grey-level digital images

Discrete sampling model.

Noise processes.

Image attributes.

Segmentation

Thresholding and thresholding algorithms.

Performance evaluation and ROC analysis.

Connected components labelling.

Region growing and region adjacency graph (RAG).

Split and merge algorithms.

Clustering algorithms

Graph based methods

Image transformations

Grey level transformations.

Histogram equalization.

Geometric transformations.

Affine transformations.

Polynomial warps.

Morphological operation

Erode and dilate as max and min operators on binary images.

Open, close, thinning and other transforms.

Medial axis transform.

Image filtering

Fourier analysis.

Linear and non-linear filtering operations.

Image convolutions.

Separable convolutions.

Sub-sampling and interpolation as convolution operations.

Feature characterisation

Calculation of region properties.

Moment features.

Boundary coding line descriptors from boundary coding and from moments.